""" Author: Benny Date: Nov 2019 """ import argparse import os from data_utils.ShapeNetDataLoader import PartNormalDataset import torch import logging import sys import importlib from tqdm import tqdm import numpy as np BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(os.path.join(ROOT_DIR, 'models')) seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat def to_categorical(y, num_classes): """ 1-hot encodes a tensor """ new_y = torch.eye(num_classes)[y.cpu().data.numpy(),] if (y.is_cuda): return new_y.cuda() return new_y def parse_args(): '''PARAMETERS''' parser = argparse.ArgumentParser('PointNet') parser.add_argument('--batch_size', type=int, default=24, help='batch size in testing') parser.add_argument('--gpu', type=str, default='0', help='specify gpu device') parser.add_argument('--num_point', type=int, default=2048, help='point Number') parser.add_argument('--log_dir', type=str, required=True, help='experiment root') parser.add_argument('--normal', action='store_true', default=False, help='use normals') parser.add_argument('--num_votes', type=int, default=3, help='aggregate segmentation scores with voting') return parser.parse_args() def main(args): def log_string(str): logger.info(str) print(str) '''HYPER PARAMETER''' os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu experiment_dir = 'log/part_seg/' + args.log_dir '''LOG''' args = parse_args() logger = logging.getLogger("Model") logger.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') file_handler = logging.FileHandler('%s/eval.txt' % experiment_dir) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) logger.addHandler(file_handler) log_string('PARAMETER ...') log_string(args) root = 'data/shapenetcore_partanno_segmentation_benchmark_v0_normal/' TEST_DATASET = PartNormalDataset(root=root, npoints=args.num_point, split='test', normal_channel=args.normal) testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=4) log_string("The number of test data is: %d" % len(TEST_DATASET)) num_classes = 16 num_part = 50 '''MODEL LOADING''' model_name = os.listdir(experiment_dir + '/logs')[0].split('.')[0] MODEL = importlib.import_module(model_name) classifier = MODEL.get_model(num_part, normal_channel=args.normal).cuda() checkpoint = torch.load(str(experiment_dir) + '/checkpoints/best_model.pth') classifier.load_state_dict(checkpoint['model_state_dict']) with torch.no_grad(): test_metrics = {} total_correct = 0 total_seen = 0 total_seen_class = [0 for _ in range(num_part)] total_correct_class = [0 for _ in range(num_part)] shape_ious = {cat: [] for cat in seg_classes.keys()} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat classifier = classifier.eval() for batch_id, (points, label, target) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9): batchsize, num_point, _ = points.size() cur_batch_size, NUM_POINT, _ = points.size() points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda() points = points.transpose(2, 1) vote_pool = torch.zeros(target.size()[0], target.size()[1], num_part).cuda() for _ in range(args.num_votes): seg_pred, _ = classifier(points, to_categorical(label, num_classes)) vote_pool += seg_pred seg_pred = vote_pool / args.num_votes cur_pred_val = seg_pred.cpu().data.numpy() cur_pred_val_logits = cur_pred_val cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32) target = target.cpu().data.numpy() for i in range(cur_batch_size): cat = seg_label_to_cat[target[i, 0]] logits = cur_pred_val_logits[i, :, :] cur_pred_val[i, :] = np.argmax(logits[:, seg_classes[cat]], 1) + seg_classes[cat][0] correct = np.sum(cur_pred_val == target) total_correct += correct total_seen += (cur_batch_size * NUM_POINT) for l in range(num_part): total_seen_class[l] += np.sum(target == l) total_correct_class[l] += (np.sum((cur_pred_val == l) & (target == l))) for i in range(cur_batch_size): segp = cur_pred_val[i, :] segl = target[i, :] cat = seg_label_to_cat[segl[0]] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if (np.sum(segl == l) == 0) and ( np.sum(segp == l) == 0): # part is not present, no prediction as well part_ious[l - seg_classes[cat][0]] = 1.0 else: part_ious[l - seg_classes[cat][0]] = np.sum((segl == l) & (segp == l)) / float( np.sum((segl == l) | (segp == l))) shape_ious[cat].append(np.mean(part_ious)) all_shape_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: all_shape_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) mean_shape_ious = np.mean(list(shape_ious.values())) test_metrics['accuracy'] = total_correct / float(total_seen) test_metrics['class_avg_accuracy'] = np.mean( np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float)) for cat in sorted(shape_ious.keys()): log_string('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat])) test_metrics['class_avg_iou'] = mean_shape_ious test_metrics['inctance_avg_iou'] = np.mean(all_shape_ious) log_string('Accuracy is: %.5f' % test_metrics['accuracy']) log_string('Class avg accuracy is: %.5f' % test_metrics['class_avg_accuracy']) log_string('Class avg mIOU is: %.5f' % test_metrics['class_avg_iou']) log_string('Inctance avg mIOU is: %.5f' % test_metrics['inctance_avg_iou']) if __name__ == '__main__': args = parse_args() main(args)